J 2022

Exploring attractor bifurcations in Boolean networks

BENEŠ, Nikola, Luboš BRIM, Jakub KADLECAJ, Samuel PASTVA, David ŠAFRÁNEK et. al.

Basic information

Original name

Exploring attractor bifurcations in Boolean networks

Authors

BENEŠ, Nikola (203 Czech Republic, belonging to the institution), Luboš BRIM (203 Czech Republic, belonging to the institution), Jakub KADLECAJ (703 Slovakia, belonging to the institution), Samuel PASTVA (703 Slovakia, belonging to the institution) and David ŠAFRÁNEK (203 Czech Republic, guarantor, belonging to the institution)

Edition

BMC Bioinformatics, 2022, 1471-2105

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 3.000

RIV identification code

RIV/00216224:14330/22:00125836

Organization unit

Faculty of Informatics

DOI

UT WoS

000793836800001

Keywords in English

Boolean networks; Attractor bifurcation; Symbolic computation; Software tool; type-1 interferons

Tags

International impact, Reviewed
Změněno: 28/3/2023 10:53, RNDr. Pavel Šmerk, Ph.D.

Abstract

V originále

Background Boolean networks (BNs) provide an effective modelling formalism for various complex biochemical phenomena. Their long term behaviour is represented by attractors–subsets of the state space towards which the BN eventually converges. These are then typically linked to different biological phenotypes. Depending on various logical parameters, the structure and quality of attractors can undergo a significant change, known as a bifurcation. We present a methodology for analysing bifurcations in asynchronous parametrised Boolean networks. Results In this paper, we propose a computational framework employing advanced symbolic graph algorithms that enable the analysis of large networks with hundreds of Boolean variables. To visualise the results of this analysis, we developed a novel interactive presentation technique based on decision trees, allowing us to quickly uncover parameters crucial to the changes in the attractor landscape. As a whole, the methodology is implemented in our tool AEON. We evaluate the method’s applicability on a complex human cell signalling network describing the activity of type-1 interferons and related molecules interacting with SARS-COV-2 virion. In particular, the analysis focuses on explaining the potential suppressive role of the recently proposed drug molecule GRL0617 on replication of the virus. Conclusions The proposed method creates a working analogy to the concept of bifurcation analysis widely used in kinetic modelling to reveal the impact of parameters on the system’s stability. The important feature of our tool is its unique capability to work fast with large-scale networks with a relatively large extent of unknown information. The results obtained in the case study are in agreement with the recent biological findings.

Links

MUNI/A/1145/2021, interní kód MU
Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace XI. (Acronym: SV-FI MAV XI.)
Investor: Masaryk University
MUNI/G/1771/2020, interní kód MU
Name: Computational reconstruction of mechanistic framework underlying receptor tyrosine kinase function in signal transduction (Acronym: FGFSIGMOD)
Investor: Masaryk University, INTERDISCIPLINARY - Interdisciplinary research projects